The Data-Driven Path to Consumption-Based Pricing

As AI changes how customers use software, it’s also changing what they value, what it costs to serve them, and how software producers need to monetize that value.

In The Data-Driven Path to Consumption-Based Pricing, we explore why consumption-based and hybrid pricing models are moving rapidly into the mainstream. You’ll learn why traditional seat-based and fixed-price models can create a growing gap between the value customers receive, the cost of delivering it, and the revenue you capture.

The speakers explain why successful consumption pricing starts not with a rate card, but with understanding customer value and gathering the usage data to validate your assumptions. Discover how product telemetry and licensing data can reveal which features customers actually use, where value is created, and which metrics may provide the strongest foundation for monetization.

You’ll also explore practical approaches for introducing consumption gradually, including fair-use guardrails, credits and tokens, and hybrid subscription-plus-usage models. Learn how these models can give customers greater flexibility while helping producers manage variable costs, protect margins, and create new opportunities for expansion. And you’ll see why consumption pricing should be treated as an iterative process that evolves as real-world customer data becomes available.

Watch the webinar to learn how to turn product usage data into a smarter, more sustainable consumption-based monetization strategy
 

Consumption-based pricing is becoming a strategic priority

Consumption-based pricing is moving from a niche experiment toward a mainstream monetization strategy as software becomes increasingly AI-enabled, connected, and usage-driven. The economics behind the shift are significant: 70% of surveyed AI producers say delivery costs are eroding margins, 52% are moving toward usage-based pricing to offset rising cloud costs, and 56% expect usage-based revenue to grow by 2027. The shift also extends beyond SaaS, with usage-based approaches gaining traction across cloud, on-premises, and embedded deployment models.

AI is breaking the relationship between seats and customer value

Traditional seat-based pricing assumes that customer value increases relatively predictably as more users gain access. AI and agentic workflows disrupt that relationship because a customer can automate more work and produce significantly greater outcomes without adding equivalent numbers of users. As software performs more of the work itself, pricing metrics based primarily on seats can become increasingly disconnected from the value customers actually receive.

Variable AI costs expose the weakness of fixed-price models

AI changes the cost side of the equation as well. Compute, data processing, and other infrastructure expenses can grow directly with usage, making the cost of serving customers considerably more variable. When delivery costs increase with consumption but revenue remains fixed, margins can become increasingly fragile. Consumption-based models provide a mechanism for bringing revenue into closer alignment with the changing economics of delivering AI-powered capabilities, and AI pricing strategies must adapt accordingly.

The value-capture gap is becoming a central monetization problem

Software providers can find themselves caught between two accelerating curves: customers are extracting more value from software while the cost of delivering that value is also increasing. If revenue fails to keep pace, a value-capture gap emerges where meaningful customer value is being delivered but not monetized. Closing that gap requires identifying pricing metrics that more closely reflect how customers consume and benefit from the product.

Consumption does not mean abandoning subscriptions

The emerging model is often hybrid rather than purely usage-based. Tiered subscriptions can provide predictable recurring revenue, subscriptions plus metered usage can capture incremental value, and credits or tokens can create a flexible currency for consumption. These approaches can also coexist, allowing providers to preserve the predictability customers and finance teams expect while introducing greater alignment between usage, value, and revenue.

Pricing should begin with customer value, not a rate card

One of the biggest risks is deciding what to charge per unit before establishing whether the chosen unit corresponds to customer value. A stronger sequence starts by understanding how customers derive value, validating packaging and willingness to pay, collecting evidence about actual usage, and piloting the proposed model with customers. Detailed rates and token structures come later, after real-world behavior has validated the underlying assumptions.

Data is the prerequisite for choosing the right consumption metric

A consumption strategy is only as strong as the data behind it. Product telemetry can reveal which features customers use, how frequently they use them, how long workflows take, and where adoption concentrates across different customer segments. Establishing those baselines before changing monetization is important because pricing itself can alter user behavior. Without a reliable before-and-after view, determining whether a new model improves value capture or unintentionally suppresses adoption becomes much harder.

Understanding customer value remains one of the hardest problems

Getting the underlying data and value model right remains challenging. In the cited customer research, 55% identified understanding customer value drivers as a major blocker to aligning price with value, while 42% pointed to data availability and insights. Another 36% reported uncertainty about pricing AI features, and 36% cited difficulty quantifying and communicating customer value. These challenges demonstrate why consumption-based pricing is as much an analytical and strategic transformation as a technical one.

Existing subscriptions can provide a safe foundation for experimentation

Predictability matters to customers as well as CFOs and revenue leaders. Maintaining a strong core subscription while experimenting with consumption creates a financial safety net, allowing new AI capabilities or premium functionality to be introduced without immediately exposing the entire business to the volatility of pure usage pricing. That provides room to learn how customers behave before consumption becomes a larger component of revenue.

Credits can begin as guardrails before becoming a pricing mechanism

Credits and tokens do not initially need to represent an explicit price for every action. They can instead establish fair-use boundaries around capabilities bundled into a subscription. A premium tier, for example, might include a defined allocation of AI usage. The customer retains predictable spending while the provider gains protection against extreme consumption that could otherwise erode the economics of the subscription.

Virtual currency becomes more powerful when it works across the portfolio

Tokens can evolve beyond paying for a single AI interaction or feature. A broader virtual currency could potentially cover AI capabilities, temporary access to additional modules, extra capacity, or other services across the portfolio. That makes credits a mechanism for increasing product adoption and customer choice rather than simply another way of metering one feature.

Trials offer a lower-risk environment for testing consumption concepts

Traditional time-based trials can provide broad access without revealing which capabilities prospects value. Introducing a defined pool of credits creates a natural usage boundary while generating telemetry about where prospects choose to spend their allocation. That information can improve sales conversations and provide a relatively controlled environment for testing token-based operations before applying the same model to established recurring revenue, which is why this is gaining traction as the go-to software trial license strategy for the AI era.

Hybrid models can solve temporary and seasonal capacity needs

Not every increase in usage justifies purchasing another full annual subscription. Customers may need additional capacity for only a few weeks or months because of seasonal projects or temporary workloads. A hybrid model can preserve the underlying subscription while allowing customers to purchase temporary capacity through credits or usage-based mechanisms, reducing procurement friction and capturing revenue that rigid subscription limits might otherwise leave on the table.

Consumption telemetry can create better expansion signals

Once usage is measurable, it can become an input to revenue expansion. Customers repeatedly reaching their limits may be strong candidates for a larger base subscription, particularly when sustained subscription capacity would be more economical than repeated overages. Consumption therefore becomes more than a billing mechanism. It can produce actionable signals for sales teams about when customer demand has grown enough to justify an upsell.

Low usage can reveal renewal risk before it becomes churn

The same telemetry can identify customers who purchased capacity but are not using it. Rather than discovering that problem at renewal, product and customer success teams can investigate which users or departments are achieving value and which are not. Patterns from successful power users can then inform adoption strategies elsewhere in the account, making usage intelligence valuable for protecting recurring revenue as well as expanding it.

Tokens can create an alternative to discounting

Credits introduce another commercial lever during negotiations and renewals. Instead of responding to pricing pressure exclusively with a reduction in subscription price, providers can potentially offer additional consumption capacity. The customer receives additional perceived value while the provider protects the headline price and keeps incremental customer activity within its own platform.

Customers need controls over consumption, not just visibility into it

Usage-based pricing transfers more spending uncertainty to customers, making governance an important part of the product experience. Administrators may need controls over how quickly individuals or groups consume credits across daily, weekly, or monthly periods. Giving customers visibility and control over consumption can help prevent unexpected spending while increasing confidence in adopting usage-driven offerings.

Consumption-based pricing requires continuous experimentation

There is unlikely to be a perfect pricing metric or token rate on the first attempt. Customer behavior, AI economics, competitive conditions, and perceptions of value will continue to change. Monetization infrastructure therefore needs to support controlled experiments and targeted adjustments without requiring extensive engineering work or forcing every customer onto the same model simultaneously.

The path to consumption should be iterative rather than disruptive

A practical progression begins by protecting predictable subscription revenue, establishing detailed usage baselines, identifying metrics correlated with value, and introducing consumption through controlled guardrails. Real-world customer behavior can then inform hybrid pricing, token allocations, packaging, upsell strategies, and customer controls. The objective is not to switch pricing models overnight but to create a feedback loop in which usage data continuously improves how value is packaged, priced, and captured.

Better monetization starts with measuring how customers really use the product

The fundamental advantage of a data-driven approach is the ability to model a pricing change before committing the business to it. Product usage and licensing telemetry can establish how frequently important workflows happen, which customer segments use them, and how proposed thresholds could affect adoption and revenue. Pricing teams can then evaluate alternative consumption metrics against actual behavior instead of relying primarily on assumptions about how customers receive value.